Energy-efficient recurrence quantification analysis

TL;DR

This paper presents new methods for calculating recurrence quantification analysis (RQA) measures directly from data, significantly reducing computational costs, memory usage, and energy consumption, thereby enabling more sustainable and scalable analysis of complex systems.

Contribution

It introduces strategies to compute RQA measures directly from time series, avoiding recurrence plot construction, and employs random sampling to further accelerate calculations, enhancing efficiency and applicability.

Findings

Reduced run times and memory usage in RQA calculations.

Maintained accuracy despite computational shortcuts.

Applicable to various RQA measures and large-scale data analysis.

Abstract

Recurrence quantification analysis (RQA) is a widely used tool for studying complex dynamical systems, but its standard implementation requires computationally expensive calculations of recurrence plots (RPs) and line length histograms. This study introduces strategies to compute RQA measures directly from time series or phase space vectors, avoiding the need to construct RPs. The calculations can be further accelerated and optimised by applying a random sampling procedure, in which only a subset of line structures is evaluated. These modifications result in shorter run times, less memory use and access, and lower overall energy consumption during analysis while maintaining accuracy. This makes them especially appealing for large-scale data analysis and machine learning applications. The ideas are not limited to diagonal line measures, but can likewise be applied to vertical line-based measures and to recurrence network measures. By lowering computational costs, the proposed strategies contribute to energy saving and sustainable data analysis, and broaden the applicability of recurrence-based methods in modern research contexts.